Statistics for Machine Learning

Key Features

• Learn about the statistics behind powerful predictive models with p-value, ANOVA, F-statistics.
• Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.
• Master the statistical aspect of machine learning with the help of this example-rich guide in R & Python.

Book Description

Complex statistics in machine learning worries a lot of developers. Knowing statistics helps in building strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for machine learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and make you comfortable with it. You will come across programs for performing tasks such as model, parameters fitting, regression, classification, density collection, working with vectors, matrices, and more.By the end of the book, you will understand concepts of required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problems.

What you will learn

• Understanding Statistical & Machine learning fundamentals necessary to build models
• Understanding major differences & parallels between statistics way of solving problem & machine learning way of solving problem
• Know how to prepare data and “feed” the models by using the appropriate machine learning algorithms from the adequate R & Python packages
• Analyze the results and tune the model appropriately to his or her own predictive goals
• Understand concepts of required statistics for Machine Learning
• Draw parallels between statistics and machine learning
• Understand each component of machine learning models and see impact of changing them

Table of Contents

Chapter 1. Questions
Chapter 2. Journey From Statistics To Machine Learning
Chapter 3. Parallelism Of Statistics And Machine Learning
Chapter 4. Logistic Regression Versus Random Forest
Chapter 5. Tree-Based Machine Learning Models
Chapter 6. K-Nearest Neighbors And Naive Bayes
Chapter 7. Support Vector Machines And Neural Networks
Chapter 8. Recommendation Engines
Chapter 9. Unsupervised Learning
Chapter 10. Reinforcement Learning